class_docstr: Mixer block consisting of a token- and a channel-mixing phase.
  
  Attributes:
    channels_mlp_dim: Hidden dimension of the channel mixing MLP.
    sequence_mlp_dim: Hidden dimension of the token (sequence) mixing MLP.
    dropout_rate: Dropout rate.
    stochastic_depth: The layer dropout rate (= stochastic depth).
    layer_scale: The scalar value used to initialise layer_scale. If None,
      layer_scale is not used.
  
  Returns:
    Output after mixer block.
class_docstr_tok: ["Mixer","block","consisting","of","a","token-","and","a","channel-mixing","phase",".","Attributes",":","channels_mlp_dim",":","Hidden","dimension","of","the","channel","mixing","MLP",".","sequence_mlp_dim",":","Hidden","dimension","of","the","token","(","sequence",")","mixing","MLP",".","dropout_rate",":","Dropout","rate",".","stochastic_depth",":","The","layer","dropout","rate","(","=","stochastic","depth",")",".","layer_scale",":","The","scalar","value","used","to","initialise","layer_scale",".","If","None",",","layer_scale","is","not","used",".","Returns",":","Output","after","mixer","block","."]
docstr: Merges residual connections.
docstr_tok: ["Merges","residual","connections","."]
code: def combine_branches(
      self, long_branch: jnp.ndarray, short_branch: jnp.ndarray
  ) -> jnp.ndarray:
      """Merges residual connections."""
      return long_branch + short_branch
  
file_path: scenic/scenic/projects/baselines/mixer.py
imports: from typing import Optional, Sequence
  import flax.linen
  import jax.numpy
  import ml_collections
  from scenic.model_lib.base_models.multilabel_classification_model import MultiLabelClassificationModel
  from scenic.model_lib.layers import attention_layers
  from scenic.model_lib.layers import nn_layers
label_desc: Use image node_id 1 for calling the MixerBlock obj's underlying member method code with example usage: obj.combine_branches(long_branch, short_branch) and returns: unknown